To build a basis for comparison, all models were trained using the training data provided by the task organisers and evaluated against the validation dataset. The best performing models were then submitted for evaluation against the test dataset during the task evaluation period. The training process was performed five times, using a different random seed each time. This is because varying the random seed used in fine-tuning BERT models can yield substantially different results, even if the models are the same and identical hyperparameters are used [1]}. The best performing version of each model was used for the remainder of the study.
